Stabilization of cluster centers over fuzziness control parameter in component-wise Fuzzy c-Means clustering

This paper proposes an extension of the traditional Fuzzy c-Means algorithm by allowing each component of the datapoints to independently contribute in the decision-making process of determining the cluster membership of the point. The above extension results in an improved accuracy in clustering. The second interesting issue undertaken here is to determine the optimum fuzziness control parameter for stabilization of the cluster centers. Lastly, the proposed extension helps in identifying the important dimensions in characterization of the datapoints. Experimental runs indicate an improvement in accuracy of clustering by the proposed algorithm in comparison to the traditional Fuzzy c-Means, with respect to the measure F_measure parameter by 26, 15 and 6 percentage on Colon cancer, Wine and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively.